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February 2013
Are house prices driven by a housing shortage?
Market Report
Title: Are house prices driven by a housing shortage?, Market Report, February 2013
Published by: BoverketAuthor and contact person: Alexandra Leonhard, +46 (0)455-35 33 80Co-Author: Peter Karpestam, Bengt HanssonData gathering: Marie Rosberg
Market Report February 2013 3
Introduction
Buying a house or a tenant-owned property is the biggest investment most households make and in order to be able to buy a place to live, almost all households require a loan. The average debt ratio among households with mortgages is more than twice their annual income before tax1. However, there is a great deal of variation, with young households in mortgaged tenant-owned apartments in Stockholm having an average mortgage equivalent to 4 times their annual income. As these loans are agreed using the house and its market value as security, this intensifies the positive effects for households when house prices rise and the negative effects of price falls.
The return on residential property in Sweden between 1996 and 2011 was very good, which is primarily the result of the increase in prices over this period2. House prices move in cycles of anything between 2 and 22 years in length, but over longer periods they often fluctuate around a constant average3. So what is it then that makes prices rise and fall in the short term? In this report, we answer this question by analysing price trends in all the Swedish counties. What proportion of the price increase in the various counties of Sweden can be explained by what we call fundamental variables, such as higher incomes, wealth, increased population/dwelling ratio (number of people per dwelling) or low mortgage interest rates? Prices have increased 120 per cent since 1996 in real terms. However, there are large regional differences in the rate of increase and in the price level. Figure 1 shows the counties with the highest and lowest average purchase price in 2011.
1 According to Sweden’s credit information agency, (UC) 2012.
2 BKN [Sweden’s National Housing Credit Guarantee Board] (2012), “Choices on the Housing Market”, Market report, May 2012. The return on ownership consists of both the value of living in a property (implicit rent) and capital gains or losses.
3 Bracke, P. (2011), “How long Do Housing Cycles Last? A Duration Analysis for 19 OECD Countries”, IMF Working paper, No. 11/231. He shows that upturns are often longer than downturns.
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Figure 1. House price, 2011
Stockholm county Uppsala county Skåne countyHalland county Västra Götaland countyVästerbotten county Norrbotten county
Source: Statistics Sweden
The figure shows the average price of a single-family house in the counties with the highest and lowest prices.
The price increase in the various counties of Sweden is primarily attributable to rising incomes, low interest rates and backward-looking expectations or speculation. In the counties with major cities, and neighbouring counties, an increase in the population/dwelling ratio has had an impact on price trends. Many people tend to view a housing shortage solely in terms of how many dwellings we have and how many people there are who want to live in them. However, it is important to remember that increased incomes also lead to greater demand for houses. As we grow richer, we want to consume more houses, and houses that are larger and more expensive. The current strong demand for houses and high prices are therefore primarily the result of us getting richer rather than as a consequence of overcrowding. If we look at the population/dwelling ratio, this has certainly increased somewhat in recent years, although it remains low from a historical perspective4. The most important explanation for the price increase, aside from incomes, is household expectations being backward-looking – in other words, prices are expected to continue to rise tomorrow because they rose today.
In the counties of Skåne and Stockholm, which have seen the largest price increase between 1996 and 2011, the price of a single-family house has risen by 163 and 162 per cent respectively in real terms (although the price
4 See Boverket [Sweden’s National Board of Housing, Building and Planning] (2012), Bostadsbrist ur ett marknadsperspektiv [Housing shortage from a market perspective], No. 2012:18.
Market Report February 20134
level in Skåne is lower than in Stockholm). Prices have primarily been driven up by increases in income and backward looking expectations, in other words both fundamental and speculative forces have come into play here. The population increase relative to housing construction accounts for only 21 and 12 per cent respectively of the price increase.
Figure 2. Factors that have lead to house prices toincrease in Stockholm county
IncomPopulation/dwelling ratioUser costBackward-lookingexpectationsFinancial wealth
Source: Own calculations
The figure shows the reasons for house price increases between 1996and 2011 in Stockholm county. Other variables also play a part, but theirimpact on price trends is considerably less; see table 3.
In Västra Götaland, the population/dwelling ratio is driving up prices to a lesser extent (5 per cent) than in Skåne and Stockholm. Here prices are instead influenced more by increased income. In those counties where house prices have not risen so rapidly, the increase in income generally accounts for a greater proportion of the price increase than in the regions close to the major cities.
If we exclude the counties with major cities, the link between the number of houses and the population has either been more balanced or the population has fallen at a faster rate than the number of houses. In Dalarna, for example, the population has fallen by 4 per cent while the supply of housing has increased by more than 1 per cent. In 13 of Sweden’s 21 counties, house price trends have been dampened by a fall in the population/dwelling ratio5.
5 In Uppsala, Östergötland, Kronoberg, Jönköping, Kalmar, Gotland, Blekinge, Halland, Värmland, Dalarna, Västmanland, Gävleborg, Västernorrland, Jämtland, Västerbotten and Norrbotten counties, the population/dwelling ratio has fallen since 1996.
The user cost, which changes principally as a result of mortgage interest rates falling, on average accounts for 12 per cent of the house price increases in the various counties. This means that falling interest rates (on the whole) have led to higher house prices.
The backward-looking expectations of households have a strong positive influence on house prices in all counties. Many households buy a house with the expectation that the price will continue to rise. This could be considered to be speculation, particularly as households repay their mortgages only to a very small extent6. It can give rise to the feeling that repaying the mortgage is unnecessary when the value of the house quickly grows. Furthermore, it is possible to take out larger loans or to borrow against the property in order to buy a new kitchen or car when the value of the house has increased. In this way, rising house prices result in increased lending.
How have house prices developed in relation to fundamental factors?
A lot of research has been carried out on analysing what drives house prices. Residential properties are fundamentally just like any other good and the price is dependent on supply and demand. OECD has examined the supply and demand using the following fundamental factors:
• income• borrowing costs• property taxes/charges• expected inflation• number of people needing a property• supply of residential property• construction costs
They found that Swedish house prices were 8 per cent above their fundamental value as early as 2005 and by 2006 the overvaluation had grown to 15 per cent7. Between 2006 and 2011, real house prices rose by 15 per cent in Sweden and BKN estimated that prices were 20 per cent above their fundamental value in 20088.
6 BKN (2012), “Choices on the Housing Market” and “Why repay your mortgage?”, Market reports, May and October respectively 2012.
7 Hüfner, F. and J. Lundsgaard (2007), “The Swedish Housing Market – Better Allocation Via Less Regulation”, Economics Department Working Paper, No. 558, OECD.
8 BKN (2008), “Bostaden – en riskfylld tillgång” [Property – a risky asset], Market report, December 2008.
Market Report February 2013 5
User cost
The real mortgage interest rate which we use to calculate the user cost of our houses is very important for determining whether or not the housing market is overvalued. The higher the real interest rate, the lower the equilibrium price of residential property. There are various ways of calculating the real mortgage interest rate, but the most common is to deduct the expected level of future inflation from the mortgage interest rate. The five-year mortgage interest rate, after interest cost tax deduction, is currently just over 2 per cent. With inflation rate expectations in line with the Riksbank’s inflation target, this means that the current real interest rate for mortgages is close to zero or only very slightly positive. The current low real interest rates in many countries are the result of the financial crisis. The low real interest rate in Sweden is partly a result of the very expansive monetary policy pursued by central banks around the world and partly also because global investors consider Sweden and Swedish securities to be a safe haven.
In view of the current low real interest rates, the Swedish housing market is therefore not overvalued according to the user cost model. Nevertheless, there are good arguments to suggest that today’s low real interest rates are temporary and a consequence of the economic crisis. From a longer historical perspective, the real interest rate is around 2.5 per cent. A return to “non-crisis interest rates” in the world would mean that interest rates on Swedish five-year mortgages would be between 5.5 and 6 per cent instead of the current rate of just over 3 per cent. According to the results of the user cost model, current house prices, calculated using normal or long-term equilibrium interest rates, are just over 20 per cent too high.
House prices in Sweden have more than doubled in real terms since 1996. Table 1 shows the price increase on single-family houses in the various counties of Sweden. It also illustrates how much the prices in the various counties have risen relative to increases in income, population and the number of residential properties.
Table 1: Real house price rise relative to fundamental factors between 1996 and 2011, per cent
County Price increase
since 1996
Price/income
growth*
Price/ change in
popula-tion/
dwelling ratio
Stockholm county 162 98 149
Uppsala county 133 70 139
Södermanland county 109 62 108
Östergötland county 124 69 130
Jönköping county 116 60 118
Kronoberg county 98 47 106
Kalmar county 88 37 98
Gotland county 152 77 174
Blekinge county 91 46 94
Skåne county 163 103 156
Halland county 158 85 162
Västra Götaland county 146 84 145
Värmland county 85 46 90
Örebro county 90 47 90
Västmanland county 106 61 107
Dalarna county 83 40 93
Gävleborg county 83 41 85
Västernorrland county 60 23 63
Jämtland county 87 41 99
Västerbotten county 71 28 79
Norrbotten county 63 24 70
*) Income here means employment income per capita. Employment income is income before tax and includes unemployment, sickness and parental benefit as well as pensions. It represents the situation on the labour market and is useful for the comparison between the counties. House prices have been deflated using the CPIF.
Source: Statistics Sweden
Market Report February 20136
Prices rose the most in the counties of Stockholm, Skåne and Halland. In the counties of Stockholm and Skåne, they have risen twice as fast as income per capita. Incomes in Stockholm were already higher than in the rest of Sweden in 1996, but the rate of increase up to 2011 has matched the average rate for the country as a whole. In Skåne, employment income per capita was generally equivalent to the average for Sweden and the rate of increase has been slightly lower than average. In contrast, the counties of Gotland and Uppsala have seen the most rapid increase in incomes. This resulted in house prices rising “only” 77 and 70 per cent faster than incomes respectively.
Higher incomes mean greater demand for property, as the general purchasing power of households increases and they choose to spend a greater proportion of their consumption on living, other things being equal. An increase in incomes is therefore expected to lead to rising house prices. International literature usually assumes that there is a 1:1 relationship between incomes and house prices9. This means that if incomes rise by 1 per cent, house prices will also rise by 1 per cent, assuming that the supply of housing remains constant. However, different studies have returned a wide variety of results. The OECD and IMF have conducted extensive reviews of the literature, which have shown that income elasticity is often greater than 1 when estimated for an individual country, but is closer to 1 when estimated as an average10. Elasticity also depends on the time period for which it is calculated. According to the Riksbank, Swedish households have increased their share of housing consumption relative to other consumption. This implies that price increases of houses have been higher than income increases since the 1990s due to changed preferences. Figure 3 illustrates property wealth relative to disposable income in nominal terms. We have also added a line which shows that the population/dwelling ratio fell slightly up to the 1990s and then plateaued during the first decade of this century.
9 See, for example, P. Englund (2011), “Swedish house prices in an international perspective”, in The Riksbank’s inquiry into the risks in the Swedish housing market.
10 Girouard, N., M. Kennedy,P. Van den Nord and C. André (2006) “Recent House Price Developments: The role of Fundamentals” OECD Working Paper, No. 475. Iossifov, P, M. Čihak and A. Shanhgavi (2008) “Interest Rate Elasticity of Residential Housing Prices”, IMF Working Paper, No. 08/247.
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Figure 3. Property wealth, disposable income and population/dwelling ratio
Property wealth Disposable incomePopulation/dwelling ratio (right axis)
Source: Macrobond, Statistics Sweden and own calculations
Here we see that property wealth and disposable income run in paralleluntil the year 2000, when property wealth began to increase much faster than income. At the same time, population/dwelling falls nationally.
The figure shows that property wealth (which includes the value of single-family houses and tenant-owned properties) has increased considerably more than incomes since the year 2000. Until the beginning of the 1990s, property wealth and incomes increased at the same rate. Following the financial crisis, property wealth increased more slowly than incomes, but this changed in the first decade of this century, when it increased more than incomes. The most important reason for the growth in nominal wealth in the housing stock is rising house prices and inflation, with only 15 per cent of the increase attributable to an increase in the number of properties. Despite this, it does not appear that the population/ dwelling ratio increased during the period but in fact fell, although there are major variations in the country.
Market Report February 2013 7
Population has increased the most in Stockholm (20 per cent), in spite of which prices have risen 119 per cent faster. In all counties, prices have increased roughly twice as fast as the population. In some counties, the population has fallen while prices have risen. Table 1 shows that prices have risen more compared to the population/dwelling ratio than to themselves during the time period.
Generally speaking, prices have risen considerably faster than the supply of houses. This may seem slightly surprising, given that construction firms should want to build more when house prices rise. However, the housing stock is slow moving and prices may rise in the short term as a result of construction simply not keeping pace. As Boverket’s study on housing shortages states, there was a large supply of residential properties following the crisis of the 1990s, which may explain why there was little construction during the late 1990s. The county of Skåne saw the fastest price rises relative to the supply of property, which increased by 9.3 per cent during the period. An increase in supply of 9.3 per cent is considerably higher than the average for the country of 4.4 per cent. The greatest increase in the number of residential properties (of around 15 per cent) was in Uppsala, in spite of which prices increased at about twice this rate. There are also counties where the supply of housing has fallen. In the county of Jämtland, the number of residential properties has fallen by 0.4 per cent, but the population has fallen quicker. Therefore has the population/dwelling ration decreased from 1.97 person per dwelling to 1.87, which is the lowest figure in the country. This means that prices have increased twice as much as persons per dwelling.
The prices of tenant-owned apartments have risen more than the prices of single-family houses over the last 11 years. Table 2 illustrates the price trend for tenant- owned properties relative to fundamentals. Unfortunately, there are no statistics available on tenant-owned property prices at county level before the year 2000.
Table 2: Real price rise for tenant-owned apartments relative to fundamental factors between 2000 and 2011, per cent
County Price increase
since 2000
price/income
growth*
price/ change in
popula-tion/
dwelling ratio
Stockholm county 99 71 93
Uppsala county 172 125 178
Södermanland county 392 317 387
Östergötland county 412 335 414
Jönköping county 366 289 369
Kronoberg county 358 287 362
Kalmar county 391 304 404
Gotland county 268 195 294
Blekinge county 304 245 307
Skåne county 291 234 280
Halland county 402 302 405
Västra Götaland county 283 221 280
Värmland county 434 362 442
Örebro county 239 192 236
Västmanland county 316 258 312
Dalarna county 367 295 377
Gävleborg county 298 241 297
Västernorrland county 212 167 208
Jämtland county 304 240 314
Västerbotten county 274 213 283
Norrbotten county 232 177 241
*) Income refers to employment income per capita. Prices have been deflated using CPIF.
Source: Statistics Sweden
Despite the fact that the period is four years shorter, the prices of tenant-owned properties have increased considerably more than prices for single-family houses in all counties except Stockholm. In Stockholm, tenant- owned property prices doubled during the first decade of this century, which on average means a real increase in value of over SEK 1 million per tenant-owned property.
Market Report February 20138
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Figure 4. Price on a tenant-owned apartment, 2011
Stockholm county Uppsala county Gotland countyVästra Götaland county Halland county Skåne countyDalarna county Västernorrland county
Source: Statistics Sweden
The figure shows the average price of a tenant-owned apartment in the counties with the highest and lowest prices.
In Sweden, real prices for single-family houses have risen by an average of SEK 480,000. Like house prices, the prices of tenant-owned properties have increased considerably more than the increases in income, population and property. In Jämtland the number of properties has fallen by 17, the population has fallen by 3,267, and incomes have risen by almost 16 per cent in real terms since the year 2000. In spite of this, prices have risen by 300 per cent, which means that on average the price of a tenant-owned apartment has risen from SEK 130,000 to SEK 500,000 in real terms in 11 years. In view of the fact that the prices of both houses and tenant-owned apartments have increased considerably more than can be explained by the fundamental factors (particularly with regard to tenant-owned apartments), there is reason to believe that prices are being driven by something other than these variables.
The mortgage market and house prices
The demand for residential property is affected by the various factors discussed above, but is also dependent on how expensive it is to borrow money, how easy it is to borrow and the house price expectations. In a world with good economic growth, low inflation and policy rates, it is easy to get too optimistic and to underestimate the price risk. When house prices rise, concerns about large amounts of borrowing to buy a house are lessened. Mortgage customers are not troubled with thoughts on how to repay as they assume that the property will be worth
more in the future. And lenders’ security for the loan increases as house prices rise, which reduces the lenders’ risk.
A study which examines what drives house prices in 17 developed countries shows that fundamental factors have a more long-term effect on house prices11. If you want to know what drives house prices in the short term, however, it is more relevant to examine the state of the mortgage market. The Swedish mortgage market was most similar to those of Ireland, the United Kingdom, Australia and Norway when the study was carried out. Characteristic of these are still the following:
• Variable interest rates.• Ability to withdraw equity from the property.• Loan-to-value ratios of 80-90% of the market value.• The current market value is used to determine the
value of the property at the mortgage approval stage12.
The study shows that house prices in Ireland, the United Kingdom, Australia, Norway and Sweden are influenced to a great extent by access to household credit, the ability to borrow a lot in relation to the purchase price and low variable interest rates. Their results show that if the real three-month interest rate falls by 1 percentage point, real house prices rise by 2.6 per cent over a five-year period. This is a stronger response than in those countries with a more conservative mortgage market (such as Germany, Italy and France). They also show that mortgage trends in Ireland, the United Kingdom, Australia, Norway and Sweden are highly correlated with house price trends, which means that the value of the house affects the household’s ability to take out even larger loans. This can also result in behaviour such as increasing borrowing against the house in order to buy a new kitchen, renovate the bathroom or buy a new car13. This grew more common even in Sweden during the first decade of this century.
11 Tsatsaronis, K. and H. Zhu (2004), “What drives housing price dynamics: cross-country evidence”, BIS Quarterly Review, March 2004. They find that GDP-growth can be used to measure where a country is in the business cycle, as well as the size of incomes and unemployment.
12 In Sweden, the market value (usually the purchase price, as this is an indication of the market value) is used to determine the value of the property when the bank or building society is deciding whether to approve a mortgage.In Germany, for example, they do not use the market value to determine the value of a property at the mortgage approval stage, but instead use a long-term sustainable value calculated according to special guidelines.
13 We reported on the occurrence of this behaviour on the Swedish mortgage market in our market report of October 2012, op. cit.
Market Report February 2013 9
Regulatory changes on the mortgage market in Denmark at the end of 2003 fundamentally changed the operation of the mortgage market. This also led to their mortgage market resembling those described above. The Danish Nationalbank reports that interest-only mortgages with variable interest rates quickly became popular14. The proportion of mortgages with variable interest rates doubled in 7 years and in 2010 represented 43 per cent of the Danish mortgage market. Their analysis shows that more than half of the real price increase between 1999 and 2007 was the result of the introduction of interest-only mortgages combined with the fact that many people opted for loans with variable interest rates. Denmark traditionally had a mortgage market that required repayments as well as interest rates which were fixed for long periods. Since, under normal circumstances, the yield curve turns slightly upwards, a variable interest rate is often lower than an interest rate that is fixed for a longer period, as this is subject to forecast inflation and risk premium. The variable interest rate which Danish lenders were able to offer was therefore lower than the traditional fixed interest rate and many households therefore replaced their fixed-rate loans with variable-rate ones.
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Figure 5. Real house price development 1970-2012
NOSEDKUKIE
Source: Macrobond, OECD
14 Danmarks Nationalbank (2011), Monetary Review 1st Quarter 2011, Part 2.
Quick rises in asset prices and price bubbles are not necessarily the result of access to credit, but access to credit increases leverage15.
In the USA, it was found that those regions with aggressive sub-prime lending also were those with the strongest price increases16. When the crisis hit, prices fell more strongly in these areas than in the rest of the country. In this way, aggressive lending increases house price fluctuations and prices rise and fall more strongly than they would have done if lending were not so high. We also demonstrated this in the Market report of May 201217. We showed how much greater risk homeowners bear if they have a mortgaged property. The more indebted, the greater the possibility of seeing positive or negative returns, depending on the way the market moves.
Figure 5, shows the real house price trend in countries with a mortgage market similar to the Swedish one. In three of the countries prices have fallen sharply and here you talk about house prices having burst. Since 2007, however, prices have plateaued in Sweden, which could perhaps be the beginning of a return to a more sustainable long-term price level. What is problematic, however, is that the mortgage loan stock has continued to increase.
House prices in Sweden have risen by more than 120 per cent over the last 16 years (in real terms). At the same time, the size of household mortgage loan stock has increased by over 260 per cent (in real terms). Figure 6 shows the annual change in mortgage loan stock and house prices, with the data series displaying a very similar trend even if the mortgages grow faster than house prices during the 2000s. The large increase in mortgage loan stock in 1986 is most likely the result of the financial deregulation which applied from this year onwards.
15 Fisher, I. (1933), “The debt-deflation theory of great depressions”, Econometrica, Vol. 1, No. 4.
16 Pavlov, A. and S Wachter (2009), “Subprime Lending and House Price Volatility”, University of Pennsylvania Institute for Law and Economic Research Paper, No. 08-33.
17 BKN (2012), “Choices on the Housing Market”, Market report, May 2012.
Market Report February 201310
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Figure 6. Annual change in house prices andmortgages, real terms
House prices Mortgages
Source: Statistics Sweden, Macrobond
We can also compare how mortgages have increased in relation to property wealth since the 1970s. Figure 7 shows current prices and property wealth, which is defined as the number of houses multiplied by their market value. Over time, the loan-to-value ratio becomes increasingly higher, as mortgages rise more than the housing stock.
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Figure 7. Development of property wealth andmortgages, current prices
Property wealth Mortgages
Source: Statistics Sweden, Macrobond and own calculations
In the diagram, we can see that the aggregate loan-to-value ratio isincreasing. Mortgages are increasing considerably faster than wealth inthe housing stock.
Since the 1980s, mortgage interest rates have experienced a downward trend. At the same time, the banks have become more generous with their lending and in terms of
the equity required for a mortgage to be approved.
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Figure 8. Mortgage interest rate and houseprice index, current prices
5 year 3month House price index (right axis)
Source: Macrobond
The figure shows the 3-month and 5-year mortgage interest rates expressed as an annual average from the major building societies and banks. Interest rates have fallen, making it cheaper to borrow money, while at the same time, house prices have risen.
The interest rate is of major importance in terms of lending. Sweden appears to be no exception. While the interest rate has been falling, the mortgage loan stock has grown.
Increasing house prices are self-perpetuating
If individuals and households make the decision to buy a house based on the expectation that prices will continue to rise, price increases become self-perpetuating. A distinction is often made in economic literature between rational and adaptive expectations. A household with rational expectations bases its forecasts for the future on all the information available to it at the time18. Individuals with back-ward looking expectations, or adaptive expectations, on the other hand, develop their forecast for the future only by observing the trend to date. Expectations based on past performance can lead to bubbles. House price bubbles grow and thrive when property buyers buy residential property that is already overvalued because
18 Fama was one of the first to define efficient markets. An efficient market where everyone acts rationally is defined as a market “in which prices always fully reflect available information”. Fama, E. (1970), “Efficient Capital Markets: A Review of Theory and Empirical Work”, Journal of Finance, vol. 25 pp. 383-417.
Market Report February 2013 11
they expect prices to continue to rise in the future19. We can refer to this as speculation. Speculation does not necessarily take the form seen in the USA or in Spain during the early years of this century, when households bought two residential properties – one to live in and one as an investment. The increased use of phrases such as property development and “property ladder” reflects people’s attitude towards the housing market. They expect that prices will continue to rise and that they will be able to make money by investing in property. If they invest using borrowed funds, which they never actually intend to repay but instead expect that prices will rise, thus reducing the loan relative to the value of the property, this results in an unhealthy housing market.
Whether or not people with back-ward looking expectations are rational is debatable. Since house prices run in long cycles it may be, as Englund says, rational to expect that a price rise will be followed by further price rises, since this is the easiest and best information the agent has20. From a historical perspective, house prices have normally risen or fallen over long periods21. A study of the trend over the last 16 years is sufficient to support this statement. On the other hand, 16 years is not a particularly long period of time and one could believe that those active on the market behave irrationally after all. Chow (1989) demonstrated that econometric models for the stock market work better if one assumes that those active in the market have back-ward looking expectations22. A new study which analyses the housing market in the USA also shows that models with completely rational households find it difficult to explain the perpetually recurring fluctuations on the housing market23. If, on the other hand, some households have back-ward looking expectations and base their decisions on the most recent house price trend, this provides a better explanation for price fluctuations. We have therefore chosen to include yesterday’s prices as an explanatory variable in the model below.
19 Himmelberg, C., C. Mayer and T. Sinai (2005), “Assessing High House Prices: Bubbles, Fundamentals, and Misperceptions”, Federal Reserve Bank of New York Staff Reports, No. 218.
20 Englund op. cit.
21 See for example BKN’s Market report “Housing choices” of May 2012 or P. Bracke, op. cit.
22 Chow, G.C. (1989), “Rational versus adaptive expectations in present value models”, The Review of Economics and Statistics, Vol. 71, No.3.
23 Gelain, P., K.J. Lansing and C. Mendicino (2012), “House Prices, Credit Growth, and Excess Volatility: Implications for Monetary and Macroprudential Policy”, Federal Reserve Bank of San Francisco Working Paper, No. 2012-11.
Which variables can explain regional house price trends?
In order to determine which variables explain the trend in real house prices in the various counties of Sweden since 1996, we are using a dynamic panel regression model (see appendix). Using this model, we calculate the relationship between the trend in real house prices and the trend in the following factors:
• Real employment income per capita.• Number of inhabitants and age structure.• Supply of dwellings.• User cost (mortgage interest rate, interest cost
deduction, effective property tax, operating and maintenance costs, risk premium and inflation expectations24).
• Real financial wealth per capita25.• House prices lagged 1 year (here expressed as a self-
perpetuating effect, which takes into account the fact that households have backward-looking expectations).
In addition to the above variables, we control for the effect of the mortgage ceiling introduced during autumn 2011, as well as for the abolition of the property tax and the introduction of the property duty in 2008, and also the property tax reduction from 1.5 to 1 per cent in 2001.
The results are shown in table 3. It illustrates how much real house prices rose by between 1996 and 2011 in per cent, as well as what proportion of the price increase can be explained by the different variables. In Stockholm county, for example, real house prices rose by 162 per cent between 1996 and 2011. The increase in incomes over the same period can account for 46 per cent of the price increase, while the increase in the population/ dwelling ratio had less of an impact on the price (21 per cent). When assessing the effect of the population/dwelling ratio on the price trend, we must also take into account the age structure and examine how many people are over the age of 19 and can therefore be expected to require their own place to live.
We can also see similar results in the county of Skåne.
24 See BKN (2008), “Bostaden – en riskfylld tillgång”, [Property – a risky asset], Market report, December 2008, for further details. This shows that the interest rate is the variable which has the greatest influence on the user cost.
25 Since financial wealth statistics are unavailable at county level, these have been estimated; see appendix for description.
Market Report February 201312
Table 3, Contribution of different variables to the increase in house prices between 1996 and 2011
County House price increasesince 1996( %)
In-come effect
Popula-tion/de-welling ratio and demo-graphy
User cost
Finan-cial wealth
2011 effect
Got-landeffect
Jämt-landeffect
Skåneeffect
Self-perpu-tating effect
Un- explained
Stockholm 162 0.46 0.21 0.10 0.03 -0.016 0.30 -0.08
Uppsala 138 0.57 -0.02 0.10 0.03 -0.017 0.31 0.02
Södermanland 109 0,55 0.05 0,12 0.04 -0.021 0.32 -0.05
Östergötland 124 0.54 -0.06 0.11 0.04 -0.019 0.30 0.09
Jönköping 116 0.61 0.00 0.11 0.04 -0.019 0.30 -0.02
Kronoberg 98 0.66 -0.12 0.12 0,04 -0.021 0.29 0.04
Kalmar 88 0,75 -0.19 0.12 0,04 -0.022 0,29 0.00
Gotland 152 0,58 -0.15 0,09 0.03 -0,015 0.13 0,31 0.02
Blekinge 91 0,65 -0.04 0,13 0.04 -0.023 0,30 -0.05
Skåne 163 0.42 0.12 0.09 0.03 -0.016 -0.03 0.33 0,04
Halland 158 0.56 0.02 0.09 0.03 -0.015 0.30 0.01
Västra Götaland 146 0.52 0.05 0.10 0.03 -0.017 0.30 0.02
Värmland 85 0.59 -0.09 0,13 0.04 -0.025 0.32 0.02
Örebro 90 0.61 0.04 0.13 0.04 -0.023 0.31 -0.11
Västmanland 106 0.53 0.01 0.12 0.04 -0.021 0.32 0.00
Dalarna 83 0.68 -0.19 0.13 0.05 -0.024 0.28 0.08
Gävleborg 83 0.66 -0.07 0.13 0.05 -0.025 0.31 -0.05
Västernorrland 60 0.81 -0.09 0.16 0.06 -0.030 0.33 -0.24
Jämtland 87 0.67 -0.22 0.12 0.04 -0.023 0.15 0.31 -0.05
Västerbotten 71 0.79 -0.17 0.14 0.05 -0.026 0.32 -0.11
Norrbotten 63 0.82 -0.22 0.16 0.06 -0.029 0.27 -0.06
For further details about the model, see the appendix. Please note that the figures in the columns add up to one, which means that they represent the proportion of the price increase accounted for by the various factors. To determine the exact proportion of the price increase accounted for by the different variables in the various counties as a percentage, the figures must be multiplied by the price increase. For example, to find out what proportion of the price increase in Gotland is the specific Gotland effect, the calculation is as follows: 152*0.13≈20, i.e. the Gotland effect represents 20 percentage points of the 152 per cent price increase in Gotland. The analysis below refers the shares expressed in per cent in order to be able to compare the counties with each other.
Source: Own calculations
Market Report February 2013 13
As expected, the increase in incomes accounts for a large proportion of the price increase in all counties. The weakest effect, however, is to be found in the counties of Stockholm and Skåne. Here, rising incomes have had a positive impact on prices and have helped to increase prices by 46 and 42 per cent respectively of the total price increase during the period. Compared with the other counties, the increase in incomes accounts for a smaller proportion of the price increase in Skåne and Stockholm. Incomes were of the greatest significance in counties which did not have such strong house price increases and which are located in Norrland.
Overall we find that incomes have a positive effect on house prices across the country. The coefficient for the income variable is estimated at 1.48, which means that the elasticity is greater than 1 and that if incomes increase by 1 per cent, prices will increase by 1.48 per cent (see table 5 in the appendix). This supports the assertion and calculations of the IMF, the OECD and the Riksbank that increased incomes have had an impact on house prices of more than 1:1 in Sweden in recent years26.
The increasing population/dwelling ratio and changed age structure can account for 21 per cent of the price increase in Stockholm and 12 per cent in Skåne. These calculations have been made at county level and it is likely that there will be a wide variation within the counties. The effect of population/dwelling ratio on house prices is probably the strongest in the municipalities with major cities and weaker in the outlying municipalities. In most counties, the demographic variable has a negative impact on the house price trend. And in some counties, e.g. Jönköping, it has no effect on house prices.
The user cost, where short-term 3-month mortgage interest rate tend to dominate, has had a positive effect on house prices in all counties. In the user cost, we have taken into account the conversion of the property tax into a duty and the fact that mortgaged households are able to make a deduction for their interest costs. In Västernorrland and Norrbotten, where house prices have risen the least, the falling user cost has had the greatest impact on house prices. In the counties with major cities and their neighbouring counties, as well as in Gotland, the user cost has had the least impact on house prices. In Gotland, it has long been popular to buy a house that can be used as a holiday home. We have therefore added a “Gotland
26 Englund (2011), Girouard et. al. (2006) and Iossifov et. al. (2008) op. cit.
effect”, which measures effects which may arise from households buying property in Gotland which they do not use as their permanent residence. The estimates show that this effect is statistically significant and positive, which means that 13 per cent of the house price rise in Gotland can be attributed to households registered elsewhere buying property in Gotland.
There has been a similar significant phenomenon in the counties of Jämtland and Skåne. In Jämtland, this may be because households buy property in Åre and other ski resorts, and also because of its proximity to Norway. This increases demand and drives up house prices by 15 per cent of the total rise in house prices in Jämtland. The county of Jämtland has experienced the strongest increase in prices among the counties of Norrland.
It could be assumed that there would be a similar effect in Skåne. During the first decade of this century, many Danes chose to move to Skåne, as the new bridge made commuting to Copenhagen easier and the level of house prices were lower in Malmö than in Copenhagen. However, as we demonstrated above, Danish house prices have fallen by 28 per cent since 2007, reaching 2003 levels. Skåne has been affected by this crash and during 2011, house prices fell in 76 per cent of Skåne’s municipalities, which compares with 49 per cent of the municipalities in the rest of the country.
-10
-5
0
5
10
15
2001 2003 2005 2007 2009 2011
per
cen
tag
e p
oin
ts
Figure 9. "Denmark effect"
Source: Own calculations
The figure shows that house prices in Skåne were positively affected bythe proximity to Denmark up to mid-2007. After 2007, this effect lessenedand it is now negative.
Market Report February 201314
The trend for the “Denmark effect” on Skåne is illustrated above. Until 2007, it contributed to rising house prices in Skåne by more than 13 percentage points. However, once house prices began to fall in Denmark, this effect began to decline and in 2011, the accumulated effect was minus 5 percentage points. There are indications that house prices are about to plateau in Denmark, which supports the hypothesis that the Denmark effect is played out, at least for now. Figure 9 shows the trend for the whole of Skåne county and it is likely that the effect was greater in the municipalities closest to the Öresund Bridge and Helsingborg.
In order to control for the effect of the mortgage ceiling, which was introduced in autumn 2010, we include a dummy variable for 2011. The 2011 effect is negative, albeit weak, and covers everything that happened in 2011 that is not already covered by the other variables. We must therefore exercise caution before attributing the whole of this effect to the mortgage ceiling. In the county of Stockholm, for example, we estimate that the 2011 effect had a negative impact on the real house price trend of 1.6 per cent of the total rise in house prices.
Like the Riksbank27, we find that the increase in financial wealth has had a small but significant effect on house prices. It explains 3 to 6 per cent of the increase in prices across the counties. As previously mentioned, we have been obliged to approximate the financial wealth, since wealth statistics are not available at county level.
The self-perpetuating effect accounts for a large proportion of the price increase in all counties. It also supports the idea that Swedish households have backward-looking and that price increases lead to future price increases. As we stated in the introduction, the self-perpetuating effect can also involve an element of speculation. Swedes only repay their mortgages to a very slight extent, around 1.2-1.3 per cent of the mortgage loan stock per annum which indicates an expectation that the price of the property will either increase or remain the same, resulting in the feeling that repaying the mortgage is unnecessary.
27 C.A. Claussen, M. Jonsson and B. Lagerwall (2011), “A macroeconomic analysis of house prices in Sweden”, The Riksbank’s inquiry into the risks in the Swedish housing market.
The self-perpetuating effect has a further positive effect on house prices, as it enables households to borrow even larger amounts. If the market value is high, the loan is large. If the market value of the property then rises, the household is often able to increase its loan-to-value ratio in order to buy a new kitchen or car. The introduction of the “ROT” refurbishment deduction has probably encouraged people to renovate and take out further loans on their property. In October, we showed that older people are taking out large loans and the statistics of Sweden’s credit information agency (UC) also indicate that the debt ratio is increasing among older people. The reason for this is subject to speculation, but one possible reason is that parents are helping their children to buy a house by increasing borrowing against their own house. In this manner, the loan-to-value ratio rises in the economy and the leverage increases.
Unfortunately, statistics on the prices of tenant-owned properties are only available from 2000, and the model does not produce statistically significant results. We nevertheless assume, supported by table 2 and efforts to estimate using the model, that the self-perpetuating effect is even greater and has more of an impact on tenant- owned property price trends than on house prices.
Market Report February 2013 15
Conclusions
Increased incomes are the primary reason why house prices have increased since 1996. Increased incomes lead to an increased willingness to consume larger and better residential properties and create a housing shortage if everything else remains constant. Combined with a rising population/dwelling ratio, this causes an even greater demand for residential property, however, the population/dwelling ratio is only rising in the counties with major cities. In the other counties, the correlation between the population and the supply of houses appears to be in balance or even negative. In 13 counties, house prices have been negatively affected by decreasing population/dwelling ratio.
In these counties, increases in income are an important reason for the price rises. In Norrbotten and Västernorrland, more than 80 per cent of the price increase is accounted for by increased incomes. In the counties of Skåne and Stockholm, the contribution of increased incomes to rising house prices was less than 50 per cent. We therefore come to the conclusion that housing shortages in terms of increasing incomes and population/dwelling ratio can account for more than half of the total increase in house prices between 1996 and 2011.
Falling user cost, a result of decreasing mortgage interest rates, has had a positive effect on house price trends in all counties, with the effect being roughly the same. Increased financial wealth also has a positive effect on prices, but this effect is small.
Overall, the fundamental factors account for approximately 2/3 of the increase in house prices over the last 15 years. The remaining 1/3 can be attributed to the self-perpetuating effect, which represents the actors’ backward-looking expectations and the way the mortgage market operates in Sweden. It could be said that the Swedish house price trend results in increased debt ratios, as larger loans are permitted as the market value of residential properties increases. In countries where lenders do not use the current market value of the property as a measure of the size of the security for the mortgage and which are more restrictive with regard to the withdrawal of equity, house prices and the debt ratio have not risen as rapidly as in Sweden. There are therefore factors on the Swedish mortgage market that cause debts to rise and support further price increases on the housing market.
If property prices were to fall, there is the risk that the price fall will be greater as a result of the backward- looking expectations which also prevail when prices are falling. This means that if prices fall today, they are expected to fall again tomorrow. House buyers therefore want to pay as little as possible because they believe the risk of losing money to be great, while at the same time the banks’ securities reduce in value. In other words, lending is strangled. We are able to determine that backward-looking expectations and a high level of borrowing have strong pro-cyclical effects on the housing market.
As a result of insufficient observations of tenant-owned apartment prices, we have been unable to perform the same econometric calculations as for single-family house prices. Nevertheless, we are able to confirm that the prices of tenant-owned properties have increased considerably more than single-family houses over the last 11 years and the fundamental variables in table 2 are unable to account for or keep pace with the tenant-owned apartment price trend.
Market Report February 201316
Appendix
In order to determine what drives regional house prices and what proportion of the price increases can be attributed to different variables, we have used a dynamic panel regression model. We use this to model the annual change in real house prices for each individual county as a function of the annual changes in incomes, household size, the size of the adult population, financial wealth, user cost and the change in real house prices in the previous year. The statistics come from Statistics Sweden and include annual data for all the counties of Sweden between 1993 and 2011. The equation used is as follows: ∆HPit=β1*∆INKit+β2*∆UCit-1+β3*∆BOTÄTit+β4*∆POP20it+ β5*∆NFit+β6*SKÅNEPOST2000+β7*SKÅNEPOST2007+β8*JÄMTLANDPOST2000+β9*GOTLAND+β10*ÅR2011+β11*∆HPit-1+ εit
The model is dynamic, as it includes a lag for the dependent variable as an explanatory variable. In order to calculate the parameter estimate, we have used the Andersson-Hsiao method28. There are several methods to choose from for dynamic panel data estimations, but many of them are best suited to microeconomic panels, where the number of individuals included is significantly greater than the number of time periods. In our case, the number of individuals (21 counties) almost the same as the number of time periods (19 years). A simulation study which compared various methods found that the Andersson-Hsiao method provides the least erroneous parameter estimates when the number of individuals is similar to the number of counties (=20)29. We therefore chose the Andersson-Hsiao method.
28 Andersson T.W. and C. Hsiao (1981) “Estimation of Dynamic Models with Error Components”, Journal of the American Statistical Association, Vol. 76, No. 375, page 598-608.
29 Judson, R.A. and A.L. Owen (1999) “Estimating Dynamic Panel Data Mo-dels: A practical guide for macroeconomists”, Economic Letters, Vol. 65, No.1.
Table 4: Description of variables and their abbreviations in the above equation
HPit Real house prices.
INKit Real employment income per capita. Employment income primarily compri-ses salaries, as well as unemployment benefit, sickness benefit, parental pay and pensions.
UCit-1 User cost, lagged by one period, calcula-ted accordingly: 3-month mortgage rate after tax – efficient property tax/duty + 4 – expected inflation from Prospera.
BOTÄTit The number of people per dwellin, lag-ged by one year, as the supply of housing is reported at the end of the year and in order to avoid problems of reverse causality.
POP20it The size of the population over 19 years.
NFit Real financial wealth per capita. Since wealth statistics at county level are unavailable, this has been approximated on the basis of Sweden’s total financial wealth, where we have assumed that each county’s share of the total financial wealth for any given year is the same as each county’s share of the country’s total employment income for that year.
SKÅNE-POST2000
Dummy variable for Skåne 2001-2011.
SKÅNE-POST2007
Dummy variable for Skåne 2008-2011.
JÄMTLAND-POST2000
Dummy variable for Jämtland county 2008-2011.
GOTLAND Dummy variable for the year 2011.
ÅR2011 Dummy variable for Gotland.
HPit-1 House prices the previous year.
Market Report February 2013 17
The real prices have been deflated using the CPIF and logarithms have been calculated for all variables other than dummy variables. i represents the county and t the year. ∆ represents the difference. If i=Stockholm and t=2005, ∆HPit then represents the change in the logarithm house prices in the county of Stockholm between 2004 and 2005. εit is the difference between the actual house price change and the house price change estimated by the model, i.e. the proportion of the house price change each year which the model is unable to explain.
β1 – β11 are the parameters to be estimated. These show the relationship between the real house prices and the variables included in the model. Since logarithms have been calculated for all variables, other than the dummy variables, the parameters can be roughly interpreted as elasticities. For example, if β1 = 0.5, this should be interpreted as meaning that a 1 per cent increase in incomes results in a change in real house prices of 0,5 per cent during the same year.
Table 5: Regression results from the estimates of the equation
Variable (β1 – β11) Standard deviation
t-stat P-value
∆INKit 1.48 0.07 20.07 0.00
∆UCit-1 -0.10 0.00 -27.42 0.00
∆BOTÄTit 2.95 0.09 34.48 0.00
∆POP20it 0.38 0.06 6.43 0.00
∆NFit 0.05 0.01 7.64 0.00
SKÅNE-POST2000
0.012 0.01 2.46 0.01
SKÅNE-POST2007
-0.043 0.01 -4.60 0.00
JÄMTLAND-POST2000
0.01 0.00 10.22 0.00
GOTLAND 0.0098 0.00 13.61 0.00
ÅR2011 -0.02 0.00 -8.92 0.00
∆HPit-1 0.31 0.02 13.77 0.00
Weighted and adjusted R2
0.974
Unweighted R2
0.55
No. of obser-vations
399
The parameters have been estimated using GLS, which corrects for autocorrelation and heteroscedasticity. Where there is spatial autocorrelation, we rectify this, at least partially, by including the following variables in the regression: time dummy for 2011, user cost and population (the population in the county rises or falls partly as a result of migration). The method has been used to estimate without constant or fixed effects for each county, as these theoretically disappear when variables are transformed to level form for the first difference. However, we have included fixed effects for Skåne, Gotland and Jämtland, as we found this to be justifiable, taking into account their particular characteristics. A Jarque-Bera test cannot rule out the normal distribution of the residuals and the t statistics and the estimated P values are can therefore be applied.
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